PODS解决了数据选择中“选多少”被忽视的问题，做模型训练优化的团队可以直接集成到现有方法中，无需改动评分指标，值得一试。
现有数据选择方法主要关注“选什么”，但固定选择比例，忽略了动态调整数据量的潜力。本文从优化角度揭示，选择比例会隐式调节正则化效果：低比例增强正则化，高比例保持数据覆盖和优化保真度。为此，提出PODS框架，作为轻量级即插即用模块，在训练中动态调度选择数据量，交替低比例正则化阶段和高比例恢复阶段，平衡效率与泛化。实验表明，PODS可将ImageNet-1k训练成本降低50%且提升准确率，将LLM指令微调加速2倍以上且不损失性能。
Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training
Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly focus on designing sample-importance criteria, i.e., deciding what to select, while typically fixing the selected data volume as the target ratio throughout training. Thus, they are often dynamic in sample identity but static in data volume. In this work, we revisit data selection from an optimization perspective and show that selected-data training induces an implicit regularization effect modulated by the instantaneous selection ratio. This reveals a key trade-off: lower ratios amplify selection-induced regularization, whereas higher ratios preserve data coverage and optimization fidelity. Motivated by this insight, we propose PODS, a Plug-and-play Oscillatory Data-volume Scheduling framework. Rather than introducing another sample-scoring metric, PODS serves as a lightweight module that dynamically schedules how much data to select over training. Under the target selection ratio, PODS alternates between low-ratio regularization phases and high-ratio recovery phases to exploit selection-induced regularization without sacrificing optimization stability. With its lightweight, ratio-level, and task-agnostic design, PODS is compatible with existing static and dynamic selection methods and broadly applicable across training paradigms. Experiments across various datasets, architectures, and tasks show that PODS consistently improves the efficiency-generalization trade-off, e.g., reducing ImageNet-1k training cost by 50% with improved accuracy and accelerating LLM instruction tuning by over 2x without performance degradation.